Label

A Label is the correct answer attached to a training example in supervised machine learning — like "fraud" or "not fraud," "cat" or "dog," or a numerical value like "price = $245." Labels can be produced by domain experts, crowdsourced workers (e.g., Amazon Mechanical Turk), automated heuristics, or even other AI models. Famous label-driven datasets include ImageNet (millions of crowd-labeled images), the Stanford Sentiment Treebank, and clinical datasets where doctors annotate scans. Label quality determines model quality, and biased labels can encode systemic discrimination — for example, hiring datasets where past human decisions reflect historical prejudice. AI governance frameworks require documented labeling processes, inter-rater reliability checks, and review for AI ethics concerns. Mastering this AI term is essential for AI compliance, responsible AI deployment, and effective AI risk management in every supervised-learning workflow.

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